TL;DR
This paper presents a high-resolution, deep learning-based mapping of urban and rural settlements across Africa, improving detail and accuracy over existing datasets and enabling better policy and research applications.
Contribution
It introduces a novel DeepLabV3-based framework integrating multi-source data to produce detailed 10m resolution maps of African settlements from 2016 to 2022.
Findings
Achieved 65% accuracy and 0.47 Kappa coefficient at continental scale.
Outperformed existing global products like SMOD.
Produced an open, reproducible high-resolution urban-rural dataset for Africa.
Abstract
Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the complex interactions between human activities and natural resources. Existing global urban-rural datasets such as such as GHSL-SMOD, GHS Degree of Urbanisation, and GRUMP are often spatially coarse, methodologically inconsistent, and poorly adapted to heterogeneous regions such as Africa, which limits their usefulness for policy and research. Their coarse grids and rule-based classification methods obscure small or informal settlements, and produce inconsistencies between countries. In this study, we develop a DeepLabV3-based deep learning framework that integrates multi-source data, including Landsat-8 imagery, VIIRS nighttime lights, ESRI Land Use Land Cover (LULC), and GHS-SMOD, to produce a 10m resolution…
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Taxonomy
MethodsSpatial Pyramid Pooling · Atrous Spatial Pyramid Pooling · 1x1 Convolution · Batch Normalization · Dilated Convolution · DeepLabv3
